How Investors Feel About Artificial Intelligence – from 29 AI Founders and Executives

Daniel Faggella is the founder and CEO at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and many global enterprises, Daniel is a sought-after expert on the competitive strategy implications of AI for business and government leaders.

Thankfully, our hundreds of interviews (on our podcast and otherwise) have given us a wide network of executives and founders in AI-related companies. We reached out to 35 such company leaders to share their experiences raising funds.

You can see a full list of the answers to our “AI Investor Sentiment” question below in our large infographic.

On the aggregate, it seems as though most of the companies report a relatively positive investor sentiment around their venture, and it seems clear that part of the challenge for AI companies today include (a) providing investors with evidence that AI has changes since the last wave of AI hype, and (b) explaining artificial intelligence in a way that convinces investors of both it’s business application and it’s basic functions in the company.

The future sentiment around AI investments may have as much to do with the success of today’s companies (nothing raises confidence like success) as it does with the improvements in specific AI use-cases in business. It will be interesting to see how these trends change in the coming years.

On Monday, The White House announced plans to co-host four upcoming public workshops on various AI topics to "spur public dialogue on artificial intelligence and machine learning and identify challenges and opportunities related to this emerging technology." Spearheaded by the Office of Science and Technology Policy, the workshops will be rolled out over the next few months (May to July) and will cover topics including implications in law and government, as well as the social and economic impacts. Workshop co-hosts include academic and non-profit institutions, as well as the National Economic Council. In addition, a new National Science and Technology Council (NSTC) subcommittee on machine learning and artificial intelligence will meet for the first time next week. The NSTC is currently working to leverage AI and machine learning technology in a variety of government services.

1 - Google’s New DeepDream Algorithm Could Foreshadow a Creative Artificial Intelligence Google just open sourced the codes to its engineers’ algorithm DeepDream, which was designed to examine how neural networks work. The result is a program that, it turns out, creates some wacky and whimsical art, taking ordinary images and enhancing those with patterns of images that it has learned. Could an ability to ‘create perception’ one day evolve into an AI force that shapes its own image of reality? (Full article on BloombergView) 2 - Can Merging AI with the Internet of Things Result in a Peaceful Union? AI Pioneers John Underkoffler, the chief executive of Oblong Industries, Professor Sanjay Sarma, the director of digital learning at MIT, and others believe we’re not asking the right questions when it comes to the inevitable future of merging AI and the Internet of Things. Underkoffler admits to being an optimist in this sphere:…millions of new objects all connected to the internet – wow, to make sense of that is going to require an incredible new interface, how do we talk to all these objects in a coherent way? That’s a really great design problem.Instead of fearing the advance of more connected AI, we should be considering the many opportunities that could advance society for the good. In order to do so, we have to ask and answer tough questions about digital architecture, customer privacy, and “artificial stupidity.” (Full article on The Guardian) 3 - Ad Campaign Uses AI to Evolve Reactions Based on Viewer M&C Saatchi has developed a new ad campaign that evolves based on viewer responses. The ad, shown in one location in London, uses a genetic algorithm based on various ad components and analyzes strengths and weaknesses. Following a similar existing design-based approach for websites, the data collected by the algorithm will be able to reveal which ad “genes” were successful and which were not, and future ads will be modified based on such input. The intent is for such technology to be use such data to virtually create ads on its own. (Full article on TechTimes) 4 - Hitachi Ltd. Develops Artificial Intelligence that Reasons Japanese-based company Hitachi Ltd. revealed an artificial intelligence software that makes “reasoned responses” to sensitive topics, supported by evidence drawn from big data. The project was launched in collaboration with researchers at Tohoku University and draws on a database of millions of articles and an index capable of 250 million correlations. A portion of the system was tested at an international forum and deemed to be more accurate in diagnosing and finding a disease than existing systems. Developers foresee users one day having logical debates with the software. (Full article on The Japan Times) 5 - Studying the Human Genome with Artificial Intelligence University of Toronto has spearheaded the new entity Deep Genomics, which will use deep machine learning capabilities with artificial intelligence to study the human genome and the potential of disease. The initiative is a new mover in the area of precision medicine. CEO Brendan Frey likens its algorithm-based approach to a “…Google search engine for genomics.” The algorithms specifically look at mutations of cells in an individual that could potentially develop into a disease. (Full article on MedCityNews)

Toyota is joining the artificial intelligence race, with a recent announcement that has a five-year plan to invest $1 billion into a Silicon-valley based research and development center for machine intelligence. The entity will be organized as a new company called Toyota Research Institute. Gill Pratt, a former roboticist for DARPA, will lead the new company. The center will focus on artificial intelligence and robotics technologies, as well as technology that helps benefit the rapidly aging population. Research efforts will be honed in on developing cars that are safer and more enjoyable for humans to drive, rather than removing humans from the equation (i.e. self-driving cars). The company plans to hire 200 scientists for its new operations.

Artificial intelligence and machine learning adoption among different industries represents a new chapter in digital transformation. However, according our own research across industries, AI adoption in 2017 remains low with majority of major success stories coming only from the largest tech players in the industry (Google, Baidu, Apple, etc). McKinsey Global Institute estimates that in 2016, tech giants invested $20-30 billion into AI, while smaller companies altogether invested an estimated $6-9 billion.

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